1. Field of the Invention
The present invention relates to detection of an abnormality in a manufacturing process of a semiconductor integrated circuit. Particularly, the present invention relates to a failure detection system, a failure detection method and a computer program product for detecting the cause of a clustering failure.
2. Description of the Related Art
One of the most important problems leading to better productivity in the production of a semiconductor device such as a large-scale integrated circuit (LSI) is to improve a yield rate. In order to improve the yield rate, it is important, by analyzing a yield loss, to analyze a manufacturing process, a manufacturing apparatus or a design condition, which caused the yield loss, and to take remedial measures to avoid or prevent yield loss. However, the LSI is produced, for example, by a sequence of several hundred processes and manufacturing apparatuses. Accordingly, once a failure occurs in the LSI, it is very difficult in general to detect a reason for the failure.
Test results of electrical characteristics of a wafer conducted after completion of a wafer process in the LSI production, sometimes give an important clue for identifying the reason for the failure. This is because the wafer tests are performed on a wafer after completion of the production process. The results of the wafer test are mapped and displayed with respect to positions on a wafer surface, and failure positions on the wafer surface are detected. A representative example of such a map is a fail bit map (FBM) acquired in a memory product. In a logic product, a merged memory logic product and the like, a pass/fail map where a nondefective chip (pass) or a defective chip (fail) are mapped and displayed is acquired by the test.
A failure distribution on the wafer surface may be classified into two types in broad terms, which are: a random failure in which failures are evenly distributed regardless of positions on the wafer surface; and a clustering failure in which the failures disproportionately occur in a portion of the wafer surface. In many cases, the clustering failure is caused by a systematic factor attributable to the manufacturing process, the manufacturing apparatus and the like. The clustering failure is a major reason for a decrease in the yield rate. The failure attributable to the manufacturing process, manufacturing apparatus and the like generates a failure pattern inherent in the manufacturing process and the manufacturing apparatus on the wafer surface. Hence, a pattern analysis of the clustering failure is a clue for identifying the reason for the occurrence of the failure.
Detection of the reason for a failure in the production of an electronic device such as the LSI is implemented by tracing back into the manufacturing record of the LSI for wafers or manufacturing lots in which the same clustering failure has occurred. For example, a search is made as to whether or not there is a commonly used manufacturing apparatus for processed wafers on which the same clustering failures have occurred in the same manufacturing process. For detecting the reason for a failure, there has been proposed a method of implementing a significance test for the manufacturing apparatuses regarding characteristic quantities obtained by quantifying the clustering failures (refer to Japanese Patent Laid-Open No. 2002-359266).
In recent years, an LSI such as an application specific integrated circuit (ASIC) has been developed. In manufacturing equipment for the ASIC, a small volume production of many different items is performed, in which types of products to be produced are increased while the production volume of each type is necessarily small. In order to detect the reason for the failure of an ASIC due to a clustering failure, it is necessary to extract a commonality in the failure patterns in the entire manufacturing equipment. However, since algorithms for computing the characteristic quantity are created for each type of product, it is difficult to extract similarities from among the clustering failure patterns beyond categories of the product types. This is because chip sizes and chip layouts on the wafer surfaces differ depending on the product types, and further, because test items of electrical characteristics, which are performed in the wafer tests, also differ depending on the product types. Even if a commonality in the clustering failures is extracted from certain types of products produced in the manufacturing equipment for the small volume production of many different items, it does not mean that the failures in the entire manufacturing equipment are extracted. Therefore, even if a significance test of the manufacturing record data is performed, the cause of such an abnormality cannot be detected. Moreover, when the clustering failure is only observed in a single production type, the possibility that the occurrence of the clustering failure is not caused by the manufacturing apparatus is high. Consequently, development of measures to prevent the occurrence of the failure is delayed.